{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08e657c4-50ff-44c9-b564-667712cbfc03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 4179\n",
      "\n",
      "汇总表（与 Excel 模板一致）：\n",
      "   商品   实际   目标   及格   良好   优秀\n",
      "0  口红  653  700  600  200  200\n",
      "1  面膜  523  500  600  200  200\n",
      "2  隔离  648  600  600  200  200\n",
      "3  防晒  856  900  600  200  200\n",
      "4  精华  714  600  600  200  200\n",
      "5  面霜  785  600  600  200  200\n"
     ]
    },
    {
     "data": {
      "image/png": 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PJJ4f1eOt3+rTD1Wz2lvaun2fViycqOTJkujatRsaNOz7hG8k8JQ8PGwym8zO5Yrl31TgwWO6dStY3l6eMeqHhtnjHQd/b8jTn39t09Yd+yVJC5eu1Z/rtkoSgRQMEaXoN+JXX8mtAH9fbdi0S1L0fAyr125V3cZdXOovnDta4Q/9ADF6WBddunxdU6bN1z8Hj6lG1XIaN7K7rgfdlL+fj5q07KUt2/apcKE8+nbIlyr9ThNjTg6Q5Ihw6OLla5q7YIXSpE6ulCmSKE3q5Lp4fE2MusMHdXL+vWvPQS1culbrN+1UvcZd1aFtQ+XNndXlV/g7wcGKioqKXhcVpeDgEN24cTvGfoGE5nA49PXQKerdvaUOHjquggVyKnu2jJo64zdlyZROHds2Upg9XDmzZ1SG9Kk1dkR32WwW7dp9UBOmzJEU3Zt1/Ldf6ebNOy6f4d3c3JQqZTLlzZ1V0yb11+gJP+u7H+YpNNQeR2uAZ+O/fPYODw9Xo097KDw8XOnTpZIkrd+0Ux3aNNS585e0YvVm5c+bXVJ0yHv1WpB+mjIwxj6R+AilYIjlqzbJarNo777DSpc2pQb3b6/6Vbs+coJowEgLZo1U8dcLxlh/+vCKWOu3av+1fpn3h3M5f74catb4PeXPl0Nbtu2Vn5+PTvyzTJevXI/xK06SAD+1/3KIy/bAs1ThreLa/89R5+TlJpNbnHXd3FzLbDabTCY3pU6VXAUL5NRrr+bVqjV/a+Pm3apTq4Imj+mt76cv0PqNO5U5U9pneh7AwxyO+++vld8pqcBDx9Wt12j1HThJrZp/qHx5sqlj16Eu21gsFkVFRan6u2XkcETqTnCIwsPDXeYlARKTm5ubNv29R3Xqd9LXvdvq8L8n9cOM33QnOFg3b9q0a89BhYSGycPdpoAAP23feUDu7lbnDYLeLldMP30/SDNnL1HKFMkkRc8t9cXnH6tG1XJKnSq5Tp+5qDIVm+jW7eDEPFX8H/svn72/6jNG+w4cVv68OeTubpMkl4DLYjY719tsVh3+96Q6dR+e0KeABEAoBUMcPX5ao8bNlK+Pl4Z83UFdeoxU4KHjid0swEWYPVzDRk3XwKFTVKbka/pmQAcVL9tAklSsaAFNGt1Thd74QJIUuPO3GMNZvb08lDJFUnl7eUiSc66GUhUa69Llay51ly4Y98h5TYCElCVzet26fX/+MovFovJlX9elEzF7kzw8DM/NLXreNJvVKkVFqUOXIbp27YYkaduO/Xqj2Cuy28OVIUPqGIEWYKQ6NSto7q8rdCc4RHeCQ2S32xUaGqZz5y/HWv/BQEuSoiKjlDJFUv3680hJkpeXh/z9fLRx1XSlSJ5Uwwd11p27Q6N69BurNX9te7YnhP9bKZIn0cFdvzuXK79TUp3aNVKPvmPl4+Mpuz1CJpObbO5WmU0m+Xh7ys3NTfnyZFOmjGn0089LNGj49xoz4WdNnzxAknTr1h05IiPVs/845c+TTRUrlIgRSHXv/ImWrtig3XsPGXq++P/0Xz57/75krbJkTqfRw+73+M6WJb327j+s69dvqlSJwvq0SW1nWffeo7Xg95jzVCHxEUrBMFarRVPG99HmLXs0c/ZSpUubUqlTJdeOXf8kdtMASYox0eKj67sO3duybZ8GDp0ij7u/ytz7srPpzxkxJoj28/XRlGkL/kNrgSfTq/84rf1jqqq9W0aLlq7TF1+N0BdfjXisbf89ckrFihbQFx0aS5Lat26gbFky6Ojx0zHqbvp7dwK2Gnh8GTOkUfHXC6pFu/4u6202q1KmSOqyLjw8wtlr0Gw2ydvLU1arVVJ0D5U8ubOqTcdBSprET+3bNNCXPUfqh4n99f2M37Rn7yH9NvtbeXp6GHNi+L906fI1pc1eXvnzZteKhZM0cOgUjZ30szw9PFS9SlmFhdnliIzUoqXrtGjpOknR1667zargkFAFh4Rq1LiZd9dHPySp78CJkqQc2TIqU4Y0yp83u65dj/6RIX3aVPq8dXQgQCgFI/zXz97HT5zVV33GKFfOzLFOCzNizI//qX0wBqEUDGGxmLVk/lgVKZxPhw6f0KlD0ROcL1uxQS3aEkrh+ZQzeyaXXiRx/dIel3vDo958q1GsPaVMZtN/byTwmC5fua4RY2ao8+cfK3fOLOrSsUmcvfUsFrOWr9rsvKvNsFHTXcqLFs6nxfPHqkXbftq288AzbzvwOK5dC9KxE2dU8e03naG/h4e7KlUo4dLjRIqe0Pz9Bp0lSaXeLOwcKrJwyVqF3b1Jy649gcqZPZNu3LitjZt3y26365/AI9p4N3h90pu/AE8qNNSuj+tF35Aic8a0+nPJFFX/oJ0OHjqu3+eMcgar9/j4eGn6TwvVo99Yl/VWq0XuNpvLulVr/tanTWrrrxWudyg7dPiEvuNHMySSp/nsnT59KufcUZLUsG5VjfzmixifcWw2q9p/OUQ//bIk4RqMBEEoBUNERDi0eeteLV2+Qbv2BOrwkZMudwABnkeHj5yM0YU4Lm4mk8qVeU2j/H1VuFAeLV62Tu42qy5euqp1y6dKklKlTKYrV4OcEzQ+3HsKeNb2HziiAvlyaMXqzdq5O1AfNvpS/Xu10Vd9RyvA31cTR/VU87b91KblR0qeNECSNGFUD9WuUd5lPyaTSSaTSUsWjI3lKNG/sL9TveWzPh3Axe07IerYZZimTuyrX+Yu0+07IQrw99W0n35Xx67DnPWmTuirq3eHn0rS2vXb9V7dDurSsYny5MqqyMj7781582TTyVPnYj1eFHffwzOWM3smVa9SVtt27Nfxk2dlc7dq8tjeGjj0O10Puqk8hWu61J8yro8iYrkBy9xfV7p8Qd+x4ReNGj9TuV+t8axPAXgiT/LZ+7uxvZUubUqlTJlUfr4+WjJ/rPYHHtG2HQd0IPCoylRs6lJ/6YJxCmPqjOcSoRSeSto0KeItv/dl5kG9B4x3/m2xmPVGsVdUrnRRDRw6JaGbBySIJ/m1xtfHWydOntPCJWvk7+cjKfoL0r0PjMmTBejwnkV6o1xDXb0W9CybDcRgNpvUrlU9VatcRucuXHbOLRUcEipPD5vKliyilCmTKTg4RBcvRd+C+V5mGhEeoSnTFqh7nzGSpPeql9eQrzvo9dL1nUM+atd8W/N/WyVJqvt+ZTVr/J7BZwhE+2vjDp07f1nvViqtOfOXK0vmdFq9xvUW92nTpNCe/Ycfa3/vViylxcuih0ZFRUWpQL6cOnr8TIK3G3iYxWLWmOFdNWX6r8qZPZMk6cseIzWgV1u5u9uUPFmAAnf+5rKNn5+Pxk36RZKUMkVSJU3ipzB7uHOqjCyZ00mK7jkVEODrXL7Hzc1NNqtF5y9e4S6TSBRP8tl73m8rFeDvq6EDOyrw4DFNmbZA167fULKkAcqXJ5vOHVnlUt9mszJ1xnOKUApPxHx3uNH+bY/3D9pms+pOcIh8fbxUuuRreqVALhV5Na+KvJZPISFh2rXnoIoUzqvbt4PlcETKz89bjgiHcmTLKLPZJKvVqlNnzvM/RiSKJ/m1JlPGNPpjxQatXrtVZUoWkSTt3zZfXp6eCrPbZTZH3w1kw6ppzm2sVovqN+0WY1JpIKE5HJHKkC61/jl4TJ99PkCVKpSQJA0f1Fm1qpdXrer3e0ItnjdGu/YedC5HPtCjL1+ebBoxuLN69h/nDKQyZkij0cO6ymI2a/b85QadERC34yfPKnuW9LJYzCpcKI/GT57jUp4mdQqdOXsx1m0fnKe/3gfvKmf2TPp5bvSdnlb++bfaflZX5coUfWZtB+7Jnze7TGaThn77g74b20eSFBR0S206DlT+vNl15WpQrD2l7mlYt6q6dGyi0DB7jH17eXqoa8em6vz5xy7rzWaz3G1WNW/TTwsWMiE0jPckn72Xr9qk7p0/kY+3l64H3dInH9fSwLvzSgUeOq5KNT5zqT9/Fnfee14RSuGJ3LutZtL0peKtV+KNQlo0d4w8PKLru5lMmji6pw4ePq6FS9apZ/9xOhB4VK8UyKkVCycpzG6X44GJ68qWKiKzxSyb1apP2/R1TuAIPI8sFrPy58mm4Q/Nu/N66foKj4hQeHiEfvp+oE6fvaiihfNpxepNGjJymmw2a4wJG4GEZrVE/6/+wUnN790hr1vvUfqq72j9OGWgtm7fr1HjfpLZYla3zp/Euq9fpn8jk9mkHNkyasKoHkqTOrnSp0slq8Wsfj1ba9nKjc/+hIA41KlVQbWqvaV3yr+h2fOW653yb8pkMmnDpp3OOm5ubkqVMpnOnrskKfr9O0umtOr+RTO9WewV3bh5/0ew7Tujh4CcOn1ektSx6zB17DpMZrNZl0+uNfTc8P9n995DqlC1RaxlJpNJKZIn0b97F7ms9/H20piJP0uSho+eoeGjZ8S6/Y4Nv2jYqOn6ee6yhG00YKAC+XKoXav6+mvjDoWGhmnvvkPq2+MzTZg8R/nyZHPOFfigyVPnJ0JL8SiEUngi835dqXm/rnxkvY2bd7sEVzdv3lbRUnV14eJVl3p79h1WqizlErydwNMwubmp8+cfu/xyeO3Mepc6Dy6b7n6xL1f6dXl4uGvr9v3KkS2j0qVNqcNHTio4JFQeHjb16/m58ubOpjadBildmpT64/cJslqtGjLyB0UyJwmeMbPFHGPdvUn2b92KHsZncnNzznGWL0825cudTSdORs+j82DPkQOBx5QvT1ZlzJBG/xw8qpWrN+vYiTO6fOW6hg3sJF9vL+cE/4DRTpw8q+DgUH3auq/+WLlRKxdN0twFKxQcEuqskyJ5EtlsVmdPKYvFotBQu06fuaAbebLJ8sC/l8NHTirw0HHDzwN4FDe36JtXPNxT6vvxfR5re5PJjfdqPBee9rN39qwZNOuHQVqwcLX27jus4q8X1KDhU/XdtAUq9WZh7Tvwb6xzSrlx3T+XCKVgmIcDKeB5Y3O3adT4mRr27fRH1t2x8Rdnz8FmjWtp4dK1Cg4J1aQxvZQ3d1bNaPSFmjetrbYt6+p60C3V+PBzBQXdUlDQLdWu21Ezpnyt2jXeVvO2fbV9J3egxLNzr6fUg0JCQnXh0v335LGTftHlK9d1JzhUo4d21aXL1zRlevQwbavV6qz3aes+unU7ONbjtP9yiNq2rKtiRQvEeVc/4Fm41/Nv+85/tH1nX0lSx7YNlStnZjVp2cul7rsVSyk0NMx5s5WIiAgdPnJCP/68WIePnFTa1CmcQezG1bH3MgGMZrGYnVNoSNHD/x9kNpvV8pM6KvZ6QR3698Qj92e1WWV76G58QGJ42s/eIaFhWrR0nfoOmqTmTWrLbDYrKipKl69cl8lsUoF8OVzmppKif4SYOZs77z2PCKUA4K5qddo+dt0H71jTrFUf+flGT27eptMgXbp0VVeuBslms2nqjN81dtLPCg+PcNbfumO/ipdroE8a1dKOXYEJdwJALHIVqh5j3eSp8126sP/xwLC710p+5FLX9sCXn7gCKUm6dPma3qvxtiIjHRox5sf/0mTgidjcrS7L79Uor26dP1G/QZN06vR5+fp6a9XiyfJwtylD+tSas2CF8y6oY+9OCi3JOb9fiuRJJEllK30S6xd8s8mkM/+udAlsgWfJ3d3mEiJZHvqxweFwqHzZYoqKjNSqhyb2j03+ItyMAs+Hp/3sffbcJecNWGw2q3PKGEnycLfF2lMKzy+3JOlKck9yAADwn5nNZueXfcAonT//WG8WL6T36naQJPXq1kJZM6dX4xb3J8gd0KuNzGaT/t66V0uWr493Pr90aVJq37b5KlG+UazD9+7NKdW4RU8tXLI2wc8HeJi3l6ciHA6FxTJp+T1enh4uQ1UB4EVBKAUAAICXyn8JSN3c3OTr661bt+4451oDAADPBsP3AAAA8FL5Lz32oqKidPOBu/ABAIBnx/ToKgAAAAAAAEDCIpQCAAAAAACA4QilAAAAAAAAYDhCKQAAAAAAABiOUAoAAAAAAACGI5QCAAAAAACA4QilAAAAAAAAYDhCKQAAAAAAABiOUAoAAAAAAACGI5QCAAAAAACA4QilAAAAAAAAYDhCKQAAAAAAABiOUAoAAAAAAACGMzSUcnNzi7GuUoUS8vf3MbIZjxRbO5+VtGlSPLKOm5ubPDxssljMznUP/h3XNjab9bHbkTFDmjjLzGazrFbLY+8roWTOlNb5Wnh42FTyzVcNb4PZHP/z/KIfDwAAAACAxGJYKPVGsVe0bvlUZc6U1mV9yTde1e+zR8nPL2GDqbaf1VOSAD990b6xvhnQXmlSJ1endo0kSTOmfK233yoe57bjRnZXk4Y1/9PxC+TLoUsn1sRbp2D+nNqwcrreKvO60qVJqWtn1uvfvYtiPI7sW6xDuxep7vuVndtuWDVdl0+u1aUTa3TpxBpdObVOV06tcy5fPrlWW9fNjPfYX/duK0ny9fXWpj9nKE+uLLHWbVSvmo4dWKqj+5fE2r57j0sn1qhpo5ou27Zp8ZEG9mnrXA4I8JUUHcYd279UUnTg5OFhi3HcKeP6qGunppIkby8vfT++rzKkTx3vc2qxmOXp4a4kAX7KkjmdXn0lt6pWLq3WzT/U2OHdtGn1DHVs2zDGdm0/qxfj2qxaubR+n/1tvMd7Gn5+PurYtqF8fbxc1g/s01ZdOjaJdRt3d5vGf/uV0qdLFaOsedPamjKuT5zHq/xOSV07s955bTz8uHJqnUYP6+KyTbo0KXX55Frn8phhXdW3RytJ0vYNP2vXptnauGq69m+br98eeI48PGyy2azKliWD9vw9V5Jks1llMpl0bP9SpUqZTF+0b6yqlUu7HM9iMT8yDDaZTE8UtL4M3N1teu3VvDGuTQAAAAB4GTyz7i/XzqxX0vSlnMubt+zR4mXrtHTBeL3xVgPdvh0ih8OhHv3GalDfdkqbOrlu3rwtKfrLZ2Rk5H86flRkpL4d+qV27g5URLhD9T+s4vziW+qNVzVg8OQ4tw0NDVNoaJgkafSwLipXuqhu3rzjUidb1gzKU7imrgfdlBT9ZTwsLFxRUVGSpKAbtxQaZnfZxmQyyWIxy24PlyTt3X9YPfqN1Q+T+qtc5U90+06wchSsFr3/LBnUuEF19ew/LtY2lij/sRwOh3O5S8cmypI5vVq26+9cF1+vm+ZNais8IkKSdOvWHc2e94daNvtAn3/xTYy6P/z4m3748bc493XPuuVTFfbAOXt5eqjtZ/XUe8B4Wa0WRUVFadHcMRo4dIo2b92jMHt03cb1a6hq5dKqWud+eFWsSAEVLpRHp06f19gR3SVJ23ceUJeOTZ11bDaLJnw3R7v2HJQk9ezaQg0+qqI7d0IUHBKiAH9fRUQ49PuStbpw4YpWrflbP89bFvO1zJJBX3ZorCV//CUp+rUMDbVr5Z9/68PalZQ0ib+uXb8hm82qyMhIRUQ49F/UrFpOdWpW0NhJv8hiMctkMsluD9cv85a7hHoPXlNhYXbt3XdYy34dp/fqdtS/R08569ntETGutQdFRDh06vR5FXrjg1jLx47o7nJOVqtFFqtZYXevU0kKDbPL4XDIx9tT9rBwdes9Sus27FDd9yurVvW3nPUa16+hBnWryGwyK2WKpFq3fKpsVqveq9dBXl4eCg4J1YbNuzR2eDctX7VJ4eHR1+D3E/qq1BuF5YiMboenp4dCQkJd2mkymXT5ynUVL9tAkuTt5akxw7vqnbffVLg9XBO/n6tvRvzgrP/Vl5/qk0a1dPb8JbVo11//BB595HaP2mdcLBazhg/qrBpVyuqfg8f0aZu+OnvukkudPLmyaOmCcSpZoXGMstjkzplZ82aN0JUr15Ulc3rNmrNU3XqNkhQdJI8d0U1lSxbRxr9367P2A3Tjxu1H7hMAAAAAnicJGkpVeOsN+Xh76tdFfzrX1ar2lm7fCdHKPzdryMhp+mPlJqVIlkSHdy9SaJhddrtdfr4++qB2RUnRPQOmTFugPl9PeOp2+Pv76NSZCzJbzMqaOb0C/H1kMpt0+N+TerP4K7oTHKLDR05Kcg2K0qROLm8vT3l7eypZUn/lzJ5JdnuE+g2erLkLVrgcY/fmOc5wSZIWzhmtbFkzOMM0k8kkH28v/bt3kbOOzWrVoqXr1KbTIOe6WXOW6p+DR2N8oSxVorCyZE4X5zk+GEg9aZ08ubOqWpWyerPc/R5DI8b8qI2rpmv0+Fk6evy0c72bm5t8fb0Vbg9X5N3A7UFubpLVYtGt28GSpMjI+3WGDeokLy8Pfd2nrQb3+1ztvvhGfQdO0LCBnVS+SnNJ0a9Vu1b11ax1H+d2ZrNZ3wxor1Vr/lb/b6LDwzSpU2j2jCEq/U4TZ7tsVovLl/v+gyep/+BJkqT06VJp8bwxWrZyo/oNmiSLxaz3qpfXnr2HdPtOiMs5fN66nkaNm6nr128qW5YM+n3OtwoLC5eXl4fuBIdoxaKJkiQPd5sGfPOdfpn3xyOe+biZTCa1av6hOnQZqqRJ/PRmsULq3b2lwiMi5O3lqTvBIdq+4We5yU0eHu6qWqeNjp84K0ma+P1c+fv76O1yxV1CqUeJjHz0tfJgKJU9W0bN+G6A3G1WbVw1XZKUKlUyRUZG6ey5S3I8FBg7HNHLPt6emjlnqSZ+P1cZ0qfWr7+MVJmK90PE4OBQKSpKm7fsUZmKTVx6Rn38aQ/n335+Ptq1abY+a/+1Vv35d5xt/rpPW3l6eujNtxopY4bUmj1jqLZs26e167fr4/rV1bhBddVr2lUB/r76flwfla7YROHhEfFuF19ZfLp1bqYSbxRSldptVKRwXk0c3VPVHghZJWnE4C80esLPjxVISdKwgZ00dOQPmj5zkTJmSKPt62dpwndzdOr0eY0Y1FlpU6dQ+Sqf6v333tHAPu3UusPAx9ovAAAAADwvEjSUCg8PV+fPW6pWjfKSoofJZc2cTl/1GaMC+XLon4PHtHf/YUlSqizlnNudOrRcZSt9otNnLiRIO/z9fPVO+TeVNk0KlS1VRHv3H9bVazdU+JXcyps7qzw9PbR9w8/y9vKUl5enxk36WUNGTtM75d9UiTcK6ZUCuZQhXWq9UiCX7gSHqFfX5mrfqv5Dx/CRxXq/J9I71VsqXdqUKluqiGbOXqoM6VNr4+rpylGwmtzc3NSvRysNGz3dJXz6sHZF7d57SLv3HlKK5Emc6y8c+1OXLl9TZGSkdm+eI38/H/0y7w916z36Pz83JpNJwwd20tiJP+vs+ftfjs+eu6QJU+Zo5JAvVPPD9s5wLX26VNq58RfZwyNiDbnc3Nx08dJVFSlZ12V99SplVaJ4IRUrU1/nzl92rjebzWr0aQ/ZbBZnKNG200Bt27HfWadDmwbKmiW9MmdMq5+mRH/Rtlgt8nC3OZf9/Lz1x8pN+uKrES7HTZM6uSq+XUJNGtZQ206DtX7TThUpnFcjBn+hI0dP6eDh486eVVJ0j6yihfOrc/cRGjawo9KmTqn8RWtLklYv+U5Tpi3Qz3OXPfkTHYe2n9XV9p3/aMu2fdq85kf9MvcPvVL8fSVPFqC/1/6keo27OgPTe9zdbfLz9ZbDEanJU+fretDNJ+pNGEuWGMODQVPgwWMqX6W59m+brxJvfyxJ+mZAe127dkPfT/9VH9evHus+qlcpp3af1XX2sLpzJ0Trlk91lnt42LRi4STZw8NlNpt0+UqQan3U3mUfbm5uGjeiuxb8vlqr12yRJA3q204b/96txcv+cqlns1nVvG0/3bp1R6dOn9fO3YEqVDC31q7frqaNamrspF/099a9kqR6H76rN4u9or827oxzu3UbdsS7z7i4ubmpcYPq+qL7CB0IPKoDgUfVpGFNZcmczhkoNm5QQ8mTB2jc5F8e/WIo+t/Jkj/Wa8asxZKkU6fP69btYAX4++rmrduqVf0tVar5mY4cO60hI6dp9+bZ8vKM7okGAAAAAC+KBA2l1q7frip12mj+zOigIG3qFKpSu41u3LitxfPGyNPTQ206DlTgoeMJedgYTp0+rzETZmnqhL5atmKDbt0OVqYMafRx8x76c9n36vP1BP3482I1blBDBfJl15CR0yRJ02cu1PSZCzV8UCdt3/mPfp67TAP7tHX2lEqVMpnKlS4aZ0+Z0FC7unX+REePn3HpDdGoXjUVf71gjN5QkVFR+m5cb61a87cmT52nqLu9jEJCQtV30ETduhXd+6hOrbcVcnc4odlslpenu+x3hz3dYzabZTK5yd39/txMJjc32dytCgkJc/bq+urLTxXg76tR46Pnm8qbJ5tzWNPw0TP0VpnXNXpYV7XpGB3+nD5zQSkylX3i12DhkrXKnjWDli4Yp8jIKHl4uCt/0fdUo2pZfd27rQIPHdc/B4/ph4n9lTVzOi1YuFr9Bk1S4UJ59NmnH+i7HxYoW5b0atyipyQ5Q757IUlc3n/vHbVpUVdBN25p7IhuSpc2pRYsXK3GLXrqzNmLLr3bJGnsyG7y8/XRmqVTlC5tSmcPrk+b1NbVa0GaPX+5cmTLqCYNa+irvmOdwzOfRvp0qdTjy091+sxFbf1rpkJCwjT+u9mSonvFjJ88W4ePnFSVSqWUOlVyfT/9V0nRwdmwQZ0kSSmSBajUO0207a9ZuhMcoqioKLm722Qxm1Xx7TckRYdYDZp2118bd0jS47U5ljqeHu4uPaW++2G+c3/fDvlSwcGh8vf30b4DRyRF9/qb//sqLZ43Ri3bDdCHdSrqTnCIJn8/T1GK0uJ5Y9X+yyHa/88RlS/7urbt/MfleMmSBujbIV9q34HDzn+TNptVvy1eo5lTB8liNuu3xWucbWjV/mvntiaT6W4IdEaSlDd3VnXoMtRZvnNXoAoWyKV1G3bEud2j9hmXdGlTKkmAn/P5lqSduwP1Sv6cOn7irJInC1Cvri00e/4fqlntLa1bv10XL12Nd58Oh0MTpsxxLjduUEPXg27qQOBRFStaQMEhodqxK9BZ99Dhk8qdK4t27g6Md78AAAAA8DxJ0FDqg9oVNaBXG02fuVCvvpJba/7api1rZ+qrvmNU48P26tejlaq9W0aBh47LbDbHObzMzc3tP335L1PyNY0Y/IVatuuvYkULKEWKpNp/4F8N7tdeWTKldQ6LS50qmc5fuBLrPnx9vdS98ycyP3CXu+TJAtSwblVnKGWxmBUR4ZDFYlZkZJSuXgvSlz2+VeqUyVxCqdw5M6tj12GSosMjN7fo4VJzF6zQvF9Xyt/fRwH+vi7zAiUJ8JPNGj2ps6eHh8u+ViyapHB7uMtz5O5uk8lkUoVy9ydwd3Nzk8VqUduOg/Troj/1cf3qat60tt6p3lJ2e7gK5MuhP5d+pzffaqR/j55SRIRDTVv21tJfx2nm1EFq22mwrl2/IUn69ZdvlSWWyZZv3Q5WqQqNY30OR0+YpUuXr6noa/nVtee3ioyMlN0ero2bd7sM1+vVrYUzLAo8eEwfN++hvLmzqnnT2tq9eY7zufby9HBZXrF6s/N5dR5z/CytXrNF77/3jpInDdD4KXP0T+BRWSxmfdO/vSSp94DxziF81eq01e3bwZo+eYC+n/Grjh4/rfatG+ij9yupTv1OSpsmhZIm8Vf1qtE9+7r3GRPruT6OM2cv6rWSdRUSEqo/l36nxs17ysPDplFDoycZ/+HH35U5U1p5eLjr695tdfHSVS1e9pf+2rhDr5eupwzpU2v1ku905uxFpclW3rnfxg1qqEjhfM4g8WFRUVFKlzaly1DSB3l7eWraT7/HWB8SGubSU+oes9mk9l8Occ4pVb1KWWfZlx2aKGOGNJo6sa9Sp0ouhyNSTRvW1JTpv+r8hStKkzqFTp46p1k/fKPXy9RzziNnNpu16c8ZSpbUX6VLFFajetWUInlSHT12WpeuXNOmLXs0enhX2cMjtHT5+hhtrfdBZUVFRmrZyo3y8faU2WzWyVPnneW3bt9R9qwZ4t3uScoe5OvrrTvBIbpyNeiB4wUrTeroO2t27RT9PhIeHqHXXs2jAb3aqNGnXzl7cT3Kzo2zlTFDar1bq7UcDod8fbx05sxFl3//t27fUZrUyR9rfwAAAADwvEjQUOrwvydUp34n7d1/WJ3aNdLXQ77ToqXrZDK5yeFw6Ku+97/QL543RvnzZVdoaJh8vL209o/v5SY3eXl56NM2fbVo6Tpn3e5fNFPnzz9WrY/aa92GHbEd2sWmLXtUsUZLubvb1LhhDZWt2FS374SoU7tGGjPxZxUvWlCSlDFDGq1Ytcll2wzpUytH9kyqWKGEBg2dopIZ0qhP98/UpWMT2axWJUsW4Jzz5/yFy6pap616dm2hJg1rOCf5tpjN8vePvsvclVPrdD3opurUqiBJstlsGjNhloaNiu6B8mHtioqKitLxE2edk6t7enqo3Wf1nMOz/P18dPpM9BfsA4FHlS772zHOObaJzh/k6eEeHUq16afAg8ckSS2a1tGsOctc5ic6e/6SqtZpq9YtPnKZaNrby0Pd+4xxCQSKFS2g6ZMHxPk6VH+3rArmz6mMGdKoSuXSOnb8jCIjo1S+7OvOHjiSlCJFUn0/fYGk6CBkw6Zdyp8nu/buO6wRY2ZE10meVMMHd1anbtEhVPTwLZsK5MuhfQf+lSQVf72g5vw4VD7eXjp1+rxOnj6vMcO6ysfHS16e7nIzmWQ2mVS65Gv6tHVf7d57SBcuXtWHtSvKzWTS1Bm/SZKqVCql7FkzaMeGX7T3wGG9kj+nJk+drw/rVNT0mYt06N8TcZ7zo5y626bpMxdp/z9H5OvrrZpVy8liMat0idd08dJV+fh4aubsJRrQq43LZODS480l9rCoqOi5oOKb6Dw2cfWUslpjvwPeuxVLqd1ndTVs1HR9M+IHfTOgvbZs26fFy/5SRIRDybq1UPasGWQyuWnTlj0uoZHD4VCd+p10+co1Xbx0TVFRUfp37yJ93LyH8/qs/E5JnTh1LsZxU6dKpr49WqlT12Gy28NlMkUPC7Xb74e8YWF2eXp6xLvd45Y9zBHhiFEnLDRMnp7ustmsqvtBZX3VZ4wz+Lt48ar6dG+pSjVbxbvfe6p/0E6tm3+o78b1VonyjeRwOFwmoZeiJ6J/+PwAAAAA4HmXoKHU7r2HYqy7N4eUFB0kfDe2tzp2G6bKte5/IXvUnFJXrwXp8JGT0RMlP4aqlUqrb49W8vfzUWiYXWuXT1WyJP6a++tKfdljpPb8PVepUiZT8aIFNeCb+3fh69CmoWrXLK/g4FB9/c13+nnuMlWtXEZNP+ulLdv2KU+uLPpmQAdVf7+dy/F6Dxiv3gPGO89x6oS+CrOHq2rl0go8eExzFqxwfqF/WOV3SmjH7kCFhIbp+Mno+WdSZ42+m9nCuaM1atxPWr1262Odd3xCQsNUoVoLZ6iROVNaValUSsXvTnbu7m5T+bLFtHT5ep0+c0Fde37rsn1cd5yLr0db+vSplCypv3x8PJUpQxoF3b1T4eq1W2P0lHrYlWtBOnf+stKmSelc17Xnt85lk8kkr4fu0LZzd6DqN+mmy1eu6dbtYIWF2dWxbUMFh4Rp4NApzpCvWeP35O/nI0mqVKGERn7zhfYd+FerFk/WhYtX9H7DzkqZPKl+mT5Etet10sGdv6lHv7Ea8u00Z8+ep9WjS3PV/aCy1vy1TRtWTdPcX1cqf9H3VKVSKRV5NZ+WrdyoJg1rqFO34fLz83EJpB7k6+PlnFz+UR6cUPxx6litFlnM5hg9pdzc3GQ2m+Xr461hgzopODhUSQL8dODu8M+0aVJo3m+r1LhBDb1bsZTSpE6hSm+X0BefN9aXPUZq777DqlD+DZV881VNi+VujkeOnpI9PCLWa8psNmvd+u0x5kwymUyaNKaXli3f4BzaFxpqV3BIqJIlC3A+Rz7eXi7BUWzbPU5ZbK4H3ZSfr7ez56Qk+dx9fZIm8ZOnh7tW/rnZWX/v/sNq1rj2I/d7z5mzF9Wt92hVKP+GSpcsoouXrip5Mn+XOj7eXgp/RHgGAAAAAM+bBA2lHpQ0fakY696rUV4FC+RUcHBILFvEbdL38zTp+3mPXf/XRX9q8R9/adOfM1SnfiedPHVe61dO05wFyyVJcxas0KQxPXXz1m2XSbinTJuvb8f9pGEDOzrX5cieUa8Vyqufvh+oU6ejQ7PVS75TrpyZ9eZbjXTq9P3eHu7uNo0e1kW+Pt7q+9VwvVuxpJq07KUl88cqa5b06jtwgkJD7/fe8Pby1Ftli6nXgPFq9vF72n/giFKlTKb+vVq7TODd56vPtOD31S4B39N4sJdNn+6faeL3c51z26RJnVzjRnRTnQZXtWPXP7FuP2xgJ/XreT9M9HC3yWw2x1pXih5KV/f9yir+ekGNnfSLwsLserdiqXh7St2TM3smvVoot14tlDvO/ff5eoLW/LXNuWy3h8sRGamJo3uqbuMuunI1SKFhdoWHRw91XLpgnJav3qRR42Y6tzl56px69Burfw4e08lT53Th4lVFRUUpV47Muno9yOV4DwdSXp4eslgtunMn5LF7MG3dvl+tOgzUocPHdfrMRefwyOTJkujq3b/jOt495UoX1aB+n+vNtxo91mTnjxNK6YE6zZvUVt0PKuv4ibPOicrv3e3wTnCoUqZIoleKN9fZc5dchu9Nmbbg7rkEaPWaLar2bhmdOnNBhw6f0LHjZxR46JgG9m2nm7fuaNEDk5bfs27FD0qRLEARd5/LJAF+Wv77RDkiHbKYzbJYLcqUu5LLOff96jMlCfDTh42+cNnX7j0HVaRwPp04Gd2zKn++HDp67PQjt3tUWWwuX7muc+cv67VCebVl+z7n8XbsDtTVazcUEhomTw93Z/306VLrzNn4b+qQLGmAxo3srvpNuzmvLYcjUg6HQ4EHjykgwE9p06Rwvn/lz5tNYybEPhQZAAAAAJ5XJqMOZLGY1aVjUw0aOiXOXjdxafFJHf299icVLZzvsbfJni2jzpy9pB8m9tcv04dIkvbsiw51ZsxapNIlXotxV7Vbt4NdemkkTeIvd5tVR46d1vJVm1W+yqfOx9FjpxURcb8XS/682bXs1/FKliRADZp1k8MR/cX5zNmLqlK7jYoVKaBtf81S86a1lTpVMknRE5j/e+SULl26pto1K2j5qk0a2KetPDzcXSZF/+fgMf0yfYjSpb3fa+i/qPxOSb36Sm6NHj/Lue7q1SBNn7VIo4Z+KYslZtDk7e2pzt2Hq0jJus7H+w06a+vdL+FS3OFHiuRJtPz3Cc45fVav3aoSb3/sfMycvSTGNh7uNs1dsMLleA8+zp2/LJvNdRjZG8Ve0bRJ/TVxytwYc4VFRUWpe5/R+rhedc37abj8/aN7SgUeOq6pM37T6TMXVK7065oyrresVoveq1Fef23Y6bKPJAF+Lsf8fkJfnfhnmYq/XiDW847NitWbNO/XlbKHR6ju+5XUr0crubm5qWa1t/TXQ0NTU6VM5rJssZjl5+ejSWN6Oefoehz3hrPFx2y6/1YwbvJsVa3TVgcPH1fND9vrvbodtXN3oMpU+kTHjp/W1Ws3XOZMe9jFS9e0759/dfX6DZ0+fV77DhzW7eBgRTgcioqK0tq/tikyMlK+Pl4u271eup6y5HtXOQpWU46C1XQ96KYq1mipHAWrKUu+d5Uh5zsu51z3/cpqWLeqWn4+QGaTWd5ens7XZ+HStWr3WT15e3kqS+Z0ql6ljP5ct+WR28VXZrVa5OPtGes5L1q6Tp3bfyyLxazXX8uvooXzaf3GnQoPj9DCJWvVv1cbZc6UVq+/ll/tW9fX0uUbJEUH2V6xDLu7ei1IqVIm09CvOyhd2pT65ONaCvD31aa/dys4JFR/rtuqLh2bSooO+319vbVzD5OcAwAAAHixGBZKtW7xkSLCI/Troj9lMsV/2Id73yRLGqCc2TPJy+vx50wJPHhM79XtoPm/rVSWzOl08NBxzZw6SClTJNW4kd21as3f6tCmoQrkyxFjW5PJpPCICFWpVMplbqu41PvgXf259Dv9tXGHPvz4S4WG2mU2m5whzZmzF1W5Viv9PPcPffVlcxUrEh1ifPbph1ryx1/6ZkB7Xbh4RVmzpFOhgrnV6vMBSprEX8mS+CsiwqE585frj5UbVK1ymRjHTpsmhRrWrao3ir3yWGFfpQol9N243goJCdW8mcO19a9ZOhn4h7asm6ViRQooSYC/WjStE2O7mbOX6N8jJ12f40PH1eCT7qr49ptq0+IjZc2S3mU4nSRlSJ9KlSqU0LIVG3Tk2Gk9nFu5u9uUOlXyGEO2HnWNSJIe2KRwoTyaNXWQPvu8v36Z94dSpkiqGlXKqliRAgoOiZ6ra/feQ6pYo6WSJQtQ+TLFJEmD+rbTsf1LNffHYSqQL7vmLFihShVK6P2a72jqjF8VFRUls9ksd3ebmjSsoSnj+zy6XXGwWi1aPG+Mjv+zTMO+7igfHy/9uuhP9e/ZWuHhEVq7fruioqLk7RUdfEz49iu1+OT+a5EzeyaZ3NzUqsPXWrt++2Mf12QyOyc6j+3xXvW3YgSRwwd20s2bd3Q96KauXgtSgL+vvh3ypT5r9oF+W/xnnMdyc3NTlYolNW5EdxUvWlAN6lbT+G97qFG96vrjtwmaMWuRypctpvdqlNc/O39TiuRJZDabZbU+XqdNk8nkbGvLZu/Lz89HG1ZO0+nDK3T68AqNGBzdu2naTwuj71a3fYE2rpquOfNXOEPp+LaLr6xOzQpasmBcrO0aPnqG0qROroO7ftfCuaM1eMRUXbp8TZLUudtwXbx0VSsXTdLsGUO0as0WjZn4sySpfev6mjSmV6z7bPpZL2XNkl6b1/yoD957R+836Owcjtjn6wkqV7qoDu9ZpHEjuqtb79FPHPYDAAAAQGJzS5Ku5NPf5u4xJQnw0+7Nc/RFj5GaM3+5fv3lWxV+Jbfs4dFzoJhNZjkiHXJzc5O7u03uNqvKVW7mnKvmSSVPFqDGDWqoVrW39Pe2veo7aJJu3bqj9q0b6NMmtTVmwixNmDJHzRq/p17dWqhz9xGaMz96aF+7VvWUP2929Rs0Sd+P76NP2/TTKwVyatSQLjp/4f5Qv8yZ06lE+UY6cfKc3NzclDtXFucE4pJUMH9OrV4yWSkylXVpm7eXp+7cHb6YM3smhYSGqUrFUtq8dY+OHT+jNKlT6PCRkxo7orsK5s+hqnXa6ubN2zKZTLH2jPHwsOnQroW6ePmavuozxmXumti8+kpudenYVFu379PBwyd0/ORZnThxViF3J1l/t2Ipfd27jV4rWfexe+I0blBDdd+vpPWbdmnYqGkuQxQ/+biWoqKinJOI1675tipXKOmcU+qHif30VpnX1bhFT5eheKOHdVGVSqV1/e48VA9LkzqFOnUd5rwToiSlTJHUGQT4eHtq1ZLvtP/AEfUZOEFnzl501nvwzo/Zs2aQPTzCOQyzds23NXxgJzVu0VNr12+Xm5ubNq6arvTpUykqMkoNm32lvzbe79H059Ip+rR1Xx09fn9oWHyKFM6rw0dOOYfmDRvYSWVLF1GN9z/X2fOXlCNbRi3/faLc3KSbt+7o7arNdfnKdUnRodbrRfJr4+bdLvts3rS2ihUpoE9a9Yn1mJUqlNDgfp/HO9F5ZKRD7Tp/Iyl6oveWzT5QrY/aO4MOPz8f9eneUnVqVdAb5Rrq7LlLeu3VvBrQq42OnTij1h2i7/zXqV2j6DsdjvjBuf93K5bSxNE91LPfWE2fuUi5c2bWrB++kcViVsFiddSoXjX169FK4RGxz5/1IIvZrFlzlrncNCEuJpNJbxZ/RfawcG3dsf+R9f8rm82qUm++qouXrmn/P0ee+fG8vTxV4o1XdfTY6ce+/gAAAADgeWJIKCVJ+fJk08HDJ57q7mFPo2mjmvpj5UaXOaNqVXtLO/cEutz1q/jrBXUg8Khu3boT577MZrPMZtMj78KVkB6cNNlo/v4+LsMHnyUfb08Fh4TFCMA8PdwVGRXlvKOhUUwmkzJlTKPjJ84+sm7tmm+r1JuF1f7LIU99vOR351AKCrr11Pt4Fh4M7u5Jlzal8uXJrhWro+9YmSplMjVr/J6mTFvgnJssNiaTSdmypHe5y6PNZlWypP4xhlkCAAAAAP5/GBZKAS+buHqvAQAAAACARzNsTingZUMgBQAAAADA0yOUAgAAAAAAgOEIpQAAAAAAAGA4QikAAAAAAAAYjlAKAAAAAAAAhiOUAgAAAAAAgOEIpQAAAAAAAGA4QikAAAAAAAAY7n+0EkzBT7VAFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "\n",
    "# ===================== 1. 生成模拟数据（基于 erp_order 表） =====================\n",
    "\n",
    "def generate_erp_orders():\n",
    "    \"\"\"\n",
    "    基于 erp_order 表结构生成模拟订单数据。\n",
    "    每件商品用 quantity=1 的订单拆开，保证汇总后数量\n",
    "    与给定“实际销量”完全一致。\n",
    "    \"\"\"\n",
    "\n",
    "    fake = Faker(\"zh_CN\")\n",
    "\n",
    "    # 六个商品及其“实际销量”——与你截图中的表完全一致\n",
    "    actual_qty_map = {\n",
    "        \"口红\": 653,\n",
    "        \"面膜\": 523,\n",
    "        \"隔离\": 648,\n",
    "        \"防晒\": 856,\n",
    "        \"精华\": 714,\n",
    "        \"面霜\": 785,\n",
    "    }\n",
    "\n",
    "    products_info = {\n",
    "        \"口红\": {\"spu\": \"SPU001\", \"sku\": \"SKU001\"},\n",
    "        \"面膜\": {\"spu\": \"SPU002\", \"sku\": \"SKU002\"},\n",
    "        \"隔离\": {\"spu\": \"SPU003\", \"sku\": \"SKU003\"},\n",
    "        \"防晒\": {\"spu\": \"SPU004\", \"sku\": \"SKU004\"},\n",
    "        \"精华\": {\"spu\": \"SPU005\", \"sku\": \"SKU005\"},\n",
    "        \"面霜\": {\"spu\": \"SPU006\", \"sku\": \"SKU006\"},\n",
    "    }\n",
    "\n",
    "    stores = [\"旗舰店A\", \"旗舰店B\", \"旗舰店C\"]\n",
    "    platforms = [\"天猫\", \"京东\", \"抖音\", \"快手\"]\n",
    "    provinces = [\"上海\", \"北京\", \"广东\", \"浙江\", \"江苏\"]\n",
    "\n",
    "    rows = []\n",
    "    # ✅ 这里改成 datetime 对象，而不是字符串，避免 ParseError\n",
    "    base_start_dt = datetime(2022, 1, 1, 0, 0, 0)\n",
    "    base_end_dt = datetime(2022, 6, 30, 23, 59, 59)\n",
    "\n",
    "    order_id = 1\n",
    "\n",
    "    for prod_name, qty_total in actual_qty_map.items():\n",
    "        for _ in range(qty_total):\n",
    "            # 下单时间在 2022 上半年\n",
    "            order_time = fake.date_time_between(start_date=base_start_dt,\n",
    "                                                end_date=base_end_dt)\n",
    "            # 付款时间 >= 下单时间\n",
    "            payment_date = order_time + timedelta(\n",
    "                hours=random.randint(1, 72)\n",
    "            )\n",
    "            # 发货时间 >= 付款时间\n",
    "            shipping_date = payment_date + timedelta(\n",
    "                hours=random.randint(0, 72)\n",
    "            )\n",
    "\n",
    "            quantity = 1  # 每条订单买 1 件，后面好控制汇总\n",
    "            unit_price = round(random.uniform(80, 300), 2)\n",
    "            product_amount = round(unit_price * quantity, 2)\n",
    "            original_price = round(unit_price + random.uniform(10, 80), 2)\n",
    "\n",
    "            store_name = random.choice(stores)\n",
    "            platform = random.choice(platforms)\n",
    "            province = random.choice(provinces)\n",
    "            city = fake.city_name()\n",
    "\n",
    "            row = {\n",
    "                \"id\": order_id,\n",
    "                \"internal_order_number\": fake.uuid4(),\n",
    "                \"online_order_number\": fake.uuid4(),\n",
    "                \"store_name\": store_name,\n",
    "                \"full_channel_user_id\": fake.uuid4(),\n",
    "                \"shipping_date\": shipping_date,\n",
    "                \"payment_date\": payment_date,\n",
    "                \"payable_amount\": product_amount,\n",
    "                \"paid_amount\": product_amount,\n",
    "                \"status\": \"已发货\",\n",
    "                \"consignee\": fake.name(),\n",
    "                \"spu\": products_info[prod_name][\"spu\"],\n",
    "                \"order_time\": order_time,\n",
    "                \"province\": province,\n",
    "                \"city\": city,\n",
    "                \"platform\": platform,\n",
    "                \"sub_order_number\": fake.uuid4(),\n",
    "                \"online_sub_order_number\": fake.uuid4(),\n",
    "                \"original_online_order_number\": fake.uuid4(),\n",
    "                \"sku\": products_info[prod_name][\"sku\"],\n",
    "                \"quantity\": quantity,\n",
    "                \"unit_price\": unit_price,\n",
    "                \"product_name\": prod_name,\n",
    "                \"color_and_spec\": \"默认规格\",\n",
    "                \"product_amount\": product_amount,\n",
    "                \"original_price\": original_price,\n",
    "                \"is_gift\": \"否\",\n",
    "                \"sub_order_status\": \"正常\",\n",
    "                \"refund_status\": \"未申请退款\",\n",
    "                \"registered_quantity\": quantity,\n",
    "                \"actual_refund_quantity\": 0,\n",
    "            }\n",
    "\n",
    "            rows.append(row)\n",
    "            order_id += 1\n",
    "\n",
    "    df = pd.DataFrame(rows)\n",
    "    return df\n",
    "\n",
    "\n",
    "# ===================== 2. 汇总数据 -> 得到与 Excel 相同的表 =====================\n",
    "\n",
    "def build_summary_table(df_erp):\n",
    "    # 按商品汇总实际销量（数量求和）\n",
    "    agg_actual = (\n",
    "        df_erp.groupby(\"product_name\")[\"quantity\"]\n",
    "        .sum()\n",
    "        .rename(\"实际\")\n",
    "        .reset_index()\n",
    "    )\n",
    "\n",
    "    # 目标 / 及格 / 良好 / 优秀 —— 与截图中的表保持一致\n",
    "    target_map = {\n",
    "        \"口红\": 700,\n",
    "        \"面膜\": 500,\n",
    "        \"隔离\": 600,\n",
    "        \"防晒\": 900,\n",
    "        \"精华\": 600,\n",
    "        \"面霜\": 600,\n",
    "    }\n",
    "\n",
    "    pass_score = 600\n",
    "    good_score = 200\n",
    "    excellent_score = 200\n",
    "\n",
    "    agg_actual[\"目标\"] = agg_actual[\"product_name\"].map(target_map)\n",
    "    agg_actual[\"及格\"] = pass_score\n",
    "    agg_actual[\"良好\"] = good_score\n",
    "    agg_actual[\"优秀\"] = excellent_score\n",
    "\n",
    "    # 按商品顺序排一下\n",
    "    order = [\"口红\", \"面膜\", \"隔离\", \"防晒\", \"精华\", \"面霜\"]\n",
    "    summary = (\n",
    "        agg_actual.set_index(\"product_name\")\n",
    "        .loc[order]\n",
    "        .reset_index()\n",
    "        .rename(columns={\"product_name\": \"商品\"})\n",
    "    )\n",
    "\n",
    "    # 构造“目标表”，用于严格校验\n",
    "    summary_target = pd.DataFrame(\n",
    "        {\n",
    "            \"商品\": [\"口红\", \"面膜\", \"隔离\", \"防晒\", \"精华\", \"面霜\"],\n",
    "            \"实际\": [653, 523, 648, 856, 714, 785],\n",
    "            \"目标\": [700, 500, 600, 900, 600, 600],\n",
    "            \"及格\": [600, 600, 600, 600, 600, 600],\n",
    "            \"良好\": [200, 200, 200, 200, 200, 200],\n",
    "            \"优秀\": [200, 200, 200, 200, 200, 200],\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # 如果不完全一致，会直接抛异常，保证与你的表格一模一样\n",
    "    pd.testing.assert_frame_equal(\n",
    "        summary.reset_index(drop=True),\n",
    "        summary_target.reset_index(drop=True),\n",
    "    )\n",
    "\n",
    "    return summary\n",
    "\n",
    "\n",
    "# ===================== 3. 画图：复刻 Excel 深色堆积柱形图 =====================\n",
    "\n",
    "def plot_sales_chart(summary):\n",
    "    # 中文字体设置（如果本机没有 SimHei，可改成 \"Microsoft YaHei\"）\n",
    "    plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "    plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "    # 深色背景\n",
    "    fig.patch.set_facecolor(\"#111b3c\")\n",
    "    ax.set_facecolor(\"#111b3c\")\n",
    "\n",
    "    # 取数据\n",
    "    products = summary[\"商品\"].tolist()\n",
    "    x = range(len(products))\n",
    "\n",
    "    actual = summary[\"实际\"].values\n",
    "    target = summary[\"目标\"].values\n",
    "    pass_score = summary[\"及格\"].values\n",
    "    good_score = summary[\"良好\"].values\n",
    "    excellent_score = summary[\"优秀\"].values\n",
    "\n",
    "    bar_width = 0.6\n",
    "\n",
    "    # 堆积区：及格/良好/优秀\n",
    "    ax.bar(\n",
    "        x,\n",
    "        pass_score,\n",
    "        width=bar_width,\n",
    "        color=\"#4a90e2\",  # 较浅的蓝\n",
    "        label=\"及格\",\n",
    "    )\n",
    "    ax.bar(\n",
    "        x,\n",
    "        good_score,\n",
    "        width=bar_width,\n",
    "        bottom=pass_score,\n",
    "        color=\"#0079c2\",\n",
    "        label=\"良好\",\n",
    "    )\n",
    "    ax.bar(\n",
    "        x,\n",
    "        excellent_score,\n",
    "        width=bar_width,\n",
    "        bottom=pass_score + good_score,\n",
    "        color=\"#0050a4\",\n",
    "        label=\"优秀\",\n",
    "    )\n",
    "\n",
    "    # 实际：用更亮的蓝色、略窄一点的柱子覆盖在堆积柱前面\n",
    "    ax.bar(\n",
    "        [i for i in x],\n",
    "        actual,\n",
    "        width=bar_width * 0.4,\n",
    "        color=\"#00a2ff\",\n",
    "        label=\"实际\",\n",
    "        zorder=3,\n",
    "    )\n",
    "\n",
    "    # 目标：黄色短横线\n",
    "    for i, t in enumerate(target):\n",
    "        ax.hlines(\n",
    "            y=t,\n",
    "            xmin=i - bar_width / 2,\n",
    "            xmax=i + bar_width / 2,\n",
    "            color=\"#ffcc00\",\n",
    "            linewidth=3,\n",
    "            label=\"目标\" if i == 0 else \"\",\n",
    "            zorder=4,\n",
    "        )\n",
    "\n",
    "    # 轴与刻度\n",
    "    ax.set_xticks(list(x))\n",
    "    ax.set_xticklabels(products, color=\"white\", fontsize=12)\n",
    "\n",
    "    ax.set_ylim(0, 1200)\n",
    "    ax.set_yticks(range(0, 1201, 200))\n",
    "    ax.tick_params(axis=\"y\", colors=\"white\")\n",
    "\n",
    "    # 去掉边框颜色，统一成白色（在深蓝背景上更清晰）\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_color(\"#ffffff\")\n",
    "\n",
    "    # 标题 & 副标题\n",
    "    ax.set_title(\n",
    "        \"2022年上半年各商品销量完成情况\",\n",
    "        fontsize=20,\n",
    "        color=\"white\",\n",
    "        pad=20,\n",
    "    )\n",
    "\n",
    "    fig.text(\n",
    "        0.5,\n",
    "        0.90,\n",
    "        \"防晒整体销量最好，达到856，面霜远超目标，超额完成30%\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        color=\"white\",\n",
    "        fontsize=14,\n",
    "    )\n",
    "\n",
    "    # 图例\n",
    "    legend = ax.legend(\n",
    "        loc=\"upper center\",\n",
    "        bbox_to_anchor=(0.5, 0.82),\n",
    "        ncol=5,\n",
    "        frameon=False,\n",
    "        fontsize=11,\n",
    "    )\n",
    "    for text in legend.get_texts():\n",
    "        text.set_color(\"white\")\n",
    "\n",
    "    # 底部脚注\n",
    "    fig.text(\n",
    "        0.02,\n",
    "        0.03,\n",
    "        \"*注：数据来源于公司销售系统，统计日期截至2022.06.30\",\n",
    "        color=\"white\",\n",
    "        fontsize=10,\n",
    "        ha=\"left\",\n",
    "    )\n",
    "\n",
    "    plt.tight_layout(rect=[0, 0.05, 1, 0.88])\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ===================== 主程序 =====================\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # 1. 生成模拟订单数据\n",
    "    df_erp = generate_erp_orders()\n",
    "    print(\"模拟订单数据条数：\", len(df_erp))\n",
    "\n",
    "    # 2. 根据订单数据汇总，得到与 Excel 一致的表\n",
    "    summary_df = build_summary_table(df_erp)\n",
    "    print(\"\\n汇总表（与 Excel 模板一致）：\")\n",
    "    print(summary_df)\n",
    "\n",
    "    # 3. 画图\n",
    "    plot_sales_chart(summary_df)\n"
   ]
  },
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   "outputs": [],
   "source": []
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